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B\u2010cell epitope (BCE) prediction is a crucial process in vaccine development, but is hindered by limited efficiency and accuracy. To address this, B\u2010Epic, the first pipeline applying Transformer to predict BCEs is independently developed. B\u2010Epic's robustness is validated through multiple testing datasets, including distinguishing clinically\u2010approved vaccine targets, identifying BCEs (the Immune Epitope Database testing dataset;\n                    <jats:italic>n<\/jats:italic>\n                    = 23,888) and immunoreactive peptides (\n                    <jats:italic>Trypanosoma cruzi<\/jats:italic>\n                    peptidome;\n                    <jats:italic>n<\/jats:italic>\n                    = 239,575) with high AUCs of 0.882 and 0.945, respectively, outperforming widely used tools. Based on its superior performance, B\u2010Epic is applied to the prevention of carcinogenic pathogens. In the application to\n                    <jats:italic>Helicobacter pylori<\/jats:italic>\n                    , peptides screened by B\u2010Epic can activate B cells in experiments, suggesting their potential as vaccine targets. In another application to Epstein\u2010Barr virus, B\u2010Epic identifies pan\u2010immunoreactive peptides in a clinical cohort (\n                    <jats:italic>n<\/jats:italic>\n                    = 899). These peptides exhibit higher reactogenicity in nasopharyngeal carcinoma patients than in healthy controls (\n                    <jats:italic>n<\/jats:italic>\n                    = 140), indicating their viability as immunodiagnostic targets. Overall, B\u2010Epic utilizes self\u2010attention, high\u2010dimensional feature projection, and convolutional neural networks to autonomously extract complicated BCE features, enabling accurate BCE prediction and thereby facilitating efforts to prevent infectious diseases and cancers.\n                  <\/jats:p>","DOI":"10.1002\/advs.202508896","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T08:01:38Z","timestamp":1759824098000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["B\u2010EPIC: A Transformer\u2010Based Language Model for Decoding B Cell Immunodominance Patterns"],"prefix":"10.1002","volume":"12","author":[{"given":"Jun\u2010Ze","family":"Liang","sequence":"first","affiliation":[{"name":"Department of Experimental Research State Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Sun Yat\u2010sen University Cancer 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Yat\u2010sen University Cancer Center Guangzhou 510060 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengfeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Experimental Research State Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Sun Yat\u2010sen University Cancer Center Guangzhou 510060 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lipeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Experimental Research State Key Laboratory of Oncology in South China Guangdong Provincial Clinical Research Center for Cancer Sun Yat\u2010sen University Cancer Center Guangzhou 510060 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Experimental Research State Key Laboratory of Oncology in South China Guangdong Provincial Clinical 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